Classifying Inflammation on Intestinal Ultrasound Images and Cineloops－A Learning Curve Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Intestinal ultrasound has become a crucial tool for assessing inflammation in patients with inflammatory bowel disease, prompting a surge in demand for trained sonographers. Whereas educational programmes exist, the length of training needed to reach proficiency in correctly classifying inflammation remains unclear. Our study addresses this gap partly by exploring the learning curves associated with the deliberate practice of sonographic disease assessment, focusing on the key disease activity parameters of bowel wall thickness, bowel wall stratification, colour Doppler signal, and inflammatory fat. METHODS: Totals of 21 novices and six certified intestinal ultrasound practitioners engaged in an 80-case deliberate practice online training programme. A panel of three experts independently graded ultrasound images representing various degrees of disease activity and agreed upon a consensus score. We used statistical analyses, including mixed-effects regression models, to evaluate learning trajectories. Pass/fail thresholds distinguishing novices from certified practitioners were determined through contrasting-groups analyses. RESULTS: Novices showed significant improvement in interpreting bowel wall thickness, surpassing the pass/fail threshold, and reached mastery level by Case 80. For colour Doppler signal and inflammatory fat, novices surpassed the pass/fail threshold but did not achieve mastery. Novices did not improve in assessing bowel wall stratification. CONCLUSIONS: We found considerable individual- and group-level differences in learning curves, supporting the concept of competency-based training for assessing bowel wall thickness, colour Doppler signal, and inflammatory fat. However, despite practice over 80 cases, novices did not improve in their interpretation of bowel wall stratification, suggesting that a different approach is needed for this parameter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".